In today's medical environment, it is common for those diagnosed with chronic disorders, their medical practitioners, insurance carriers, medical researchers and scientists to have a need to provide data to and access a common medical database hierarchy. Subjective testing of those diagnosed is known such as in the diagnosis and measurement of progress of, for example, those inflicted with Parkinson disease (PD). Subjective PD testing may comprise, for example, a timed observation and video camera collection of the individual's moving arms and hands or other extremities in a requested protocol to subjectively assess their susceptibility to tremors, twitches and the like. Medical practitioners contemporaneously collect and record time of day, date, individual with disease data including age, gender, body weight, height, blood pressure, environmental conditions of the test and the like.
Subjective testing suffers from high inter-rater and intra-rater variability depending on the chosen cohort and metric (Larsen et al. 1983, van Hilten et al. 1994). By use of the term cohort in the specification and claims is a group of similarly situated individuals such as a cohort of individuals diagnosed with the same disease, a disease cohort or a cohort of caregivers or others. Subjective measurements further require the burden of observer resources for administration and data capture. Taken together, these factors in combination with a perceived potential for lowering clinical trial burden through lower required sample size and the ability to engage those individuals diagnosed with disease in their home environment have lead to improvements in application specific design offerings for measurements (Mamorita et al. 2009, Great Lakes Neurologic (video and accelerometer), APDM (gyroscope/magnetometer/accelerometer), Mcnames et al. “SYSTEM FOR DATA MANAGEMENT, ANALYSIS, AND COLLABORATION OF MOVEMENT DISORDER DATA.” U.S. patent application Ser. No. 12/763,538, filed Apr. 20, 2010), Cambridge Neurologics (a device with an accelerometer), QMAT (Voice, accelerometer, pegboard, and paddles) and techniques (Boraud et al. 1997, Dunnewold et al. 1997 & 1998, Salarian et al. 2007, Yokoe et al. 2009, Albers 2011) and early trials to associate treatment with objective measures (Hoff et al. 2001, Papapetropoulos et al. 2008, Taylor et al. 2009, Rusk et al. 2011), including in the home environment (Goetz et al. 2009).
With Apple Inc.'s (hereinafter, Apple) introduction of wireless telecommunication devices circa 2010-2011, Apple provided hardware within these devices including but not limited to an on-board camera, GPS access, a magnetometer, an accelerometer, time and date data and a gyroscope. In particular, Apple's CMMotionManager is an object gateway to raw gyroscope, magnetometer and accelerometer data, which has been used, for example, by camera users, for example, for attitude data. A poster prepared in 2011 by Dr. Sillay for presentation in Europe shows x, y, and z coordinate data from a gyroscope collected from such an APPLE® telecommunications device that may be collected and graphed to comprise objective hand movement data for use with the known Uniform Parkinson's Disease Rating Scale (UPDRS), discussed further below, to quantify Parkinson's disease symptoms such as tremors with and without brain stimulation via programmed implants, as will be discussed further below. Other devices from other vendors, such as smart phones, smart watches and tablets, for example, using the ANDROID® operating system (available from Google, Inc.), and devices designed for computer games such as the Microsoft XBOX® game console and KINECT® game console can also be used.
Efforts at objective measurement of disease are beginning to take hold with experimental (QMAT) and commercially available devices (Great Lakes Neurological, APDM) and software to make use of commercially available devices (Navigated Technologies/iMovePD motion data logger). While early efforts are underway to include such technologies in clinical trials (UW clinical trial, UCSF clinical trial), randomized clinical trial results benchmarking objective measures are lacking.
On the other hand, such objective data collection, while gatherable, may not be easily entered into known databases. The medical records database hierarchy must be periodically updated to even receive collected data from newly developed subjective/objective data collection. The data entry screens do not permit the entry of such new data or new diagnostic or progress testing, for example, by cameras or other devices. Hurdles to improvements in care of those diagnosed include, but are not limited to the acquisition, processing, and reporting of aggregate health related data from disparate sources. Data sources include multiple medical providers, caregivers, and individuals diagnosed with a disease, and each source typically utilizes a different data store, implying possibly different fields, formats, access methods, and security, privacy and data sharing legal and technical protocols.
Indeed, technical and legal barriers exist for the successful navigation of ideas to fruition, in particular, the migration of data across barriers among the individual with disease, the medical practitioner, the database hierarchy and the surrounding medical and individual with disease community. Referring briefly to FIG. 1 (Prior Art), there is depicted an overlapping hierarchy similar to a Venn diagram on the left whereby the University of Wisconsin 105 is shown distinct from the University of Wisconsin Medical Foundation 110, the University of Wisconsin Hospital and Clinics 115, the medical doctor 120, a given device for data collection or disease control 155 (such as a body implant for drug release or electrode stimulation or a gyroscope motion data collector of an iPhone) which may comprise a memory and transceiver, clinic nurse practitioners 165, a development team 145, biomedical engineering (BME) students 150, clinic nurses 170 and individual with disease care 160 shown. To the right of FIG. 1 is seen an IP designee 125 interfacing with a non-University of Wisconsin (UW) tech transfer entity 130 to a software distribution platform 135 to a further overlapping Venn diagram showing a individual with disease user 140, the device 180 and its software 170 tied back to the UW hospital and clinics 115. Clearly in such a complex medical and individual with disease environment with impediments to data flow, the hierarchy and legal and technical structure tends to most importantly limit individual with disease development and progress if not fail to promote research and development of cures, an individual with disease social network, the need for an insurance carrier to collect individual with disease data and the like.
Parkinson's disease is used in the context of many neurological diseases or other conditions in the present discussion by way of example of many such diseases or conditions ranging from epilepsy, Alzheimer's disease, multiple sclerosis, essential tremor, dystonia, normal-pressure hydrocephalus, spinal and gate disorders to stroke. Parkinson's disease (PD), alone, impacts the quality of life of one percent of the adult population over sixty years of age.
A Parkinson's disease questionnaire (PDQ) is known that is computer-based that a user with disease may navigate and particularly complete to quantify and qualify their symptoms and other data at a given date and time. Referring briefly to FIGS. 5 and 6 (Prior Art), there are shown a symptoms questionnaire (FIG. 5) comprising, for example, diagnosis date 510, first symptom 520, onset of medical treatment and what treatment 530, first use of levodopa or related drug regimen 540, date of significant impact of PD on individuals with diseases' work/home life 550 and first consideration of surgical therapy such as a neuromodulator implant 560. Per FIG. 6, (Prior Art), examples of demographic data collection on the PDQ may include Zip code 610, name of primary care physician 620, neurologist 630, and neurosurgeon 640.
A biometrics information telecommunications software application is known from US Published Patent Application No. 2012/0148115 of Jun. 14, 2012 and is but one example of a plethora of software applications that have been developed for mobile telecommunications devices, in this case, to collect biometrics data such as photographs of individuals, fingerprints, location and other data with respect to a particular time and date, location and particular user such as a first responder.
PD burden is often measured by medical clinicians who subjectively grade the degree, for example, of forearm slowness and pronation/supination movement (a movement similar to screwing in a light bulb) and rated according to a known Uniform Parkinson's Disease Rating Scale (UPDRS) described a quarter century ago (Fahn, et al. 1987). In the clinical setting, the individual diagnosed with disease may be captured on camera as the person goes through the movements in response to the requests of the clinicians. The current method of data inclusion in medical databases for the most part excludes or is not permissive of accepting video data, let alone the subjective data collection such as UPDRS data or the PDQ data completed by the individual with disease. Expanding the use of clinical measures (CM) such as the UPDRS to address population health questions is stymied by rater variability, subjectivity, and provider burden of effort.
An exemplary medical database is one known from US Published Patent Application No. 2008/0208914, published Aug. 28, 2008, in which it is suggested that an individual with disease (IWD) portal may provide IWD access to their medical records. Referring briefly to FIG. 6, FIG. 15 and FIG. 24, the IWD's doctor may also have unrestricted access to her individual with disease's records 1570. But this doctor 2410 may not have access to another doctor's records for a similarly situated individual with disease 2420. Per FIG. 15 and discussion within the published application, the individual data of those diagnosed with disease may be permissively uploaded (with some healthcare data protection) to such entities as the Center for Disease Control (for controlling a possible epidemic), the National Institutes of Health 1520, state level and university level databases for other purposes such as developmental purposes. However, this suggested ideal may not be practical unless the databases are compatible, legally and technically.
Surgical interfaces to medical databases are likewise lacking. These databases may be doctor based, hospital based, maintained at a state level, a regional level or a national or (federal) National Institutes of Health (NIH) or Center for Disease Control (CDC) level 1520. The databases in the hierarchy may not be capable of data sharing, retrieval and query by parties that could use the data for research, progress of individuals with disease, social networking, insurance or other purposes as in FIG. 24.
Neuromodulation is known in restorative neuroscience and functional neurosurgery whereby, by example, a deep brain stimulator may be implanted in the human brain. FIG. 2 is a PRIOR ART data flow diagram for a candidate for neuromodulation (DZ) 250 as an example of a disease being managed with best medical therapy (BMT) 240 or treatment 220 (in this case with deep brain stimulation [DBS] and the comparative effectiveness or efficacy being compared with both subjective measures (SM) 230 and objective measures (OM) 210 in a manual setting of today. Referring briefly to FIG. 3 (Prior Art), there is shown a data flow chart for an individual diagnosed with disease who may benefit from neuromodulation. At step 300, preoperative data is collected at a healthcare provider appointment and entered into a protected health record. At step 310, surgical planning data is collected involving the neurosurgeon and the neurosurgeon's team and maintained at a surgical planning station. At step 320, data from the surgery itself may be collected including photographic or video data and monitored and stored individual with disease data such as blood pressure and brain activity during surgery. At step 330, the individual with disease has their new neuromodulator programmed according to a best projection of how the neuromodulator should perform. At step 340, there is an opportunity for medical and community follow-up of those with disease and feedback as to how the individual diagnosed with disease and subsequently implanted with a neuromodulator is performing with the programmed neuromodulator and whether different programming is appropriate, for example, in combination with L-dopa or another drug regimen or combination of drug therapies as is known in the art.
Yet, data from the surgical room where the implant is performed are not automatically transferred to an electronic health record, which might include a surgical plan, subjective or objective documentation of the procedure, or video record of the surgery itself. In the case of neuromodulation in the restorative neurosciences and functional neurosurgery to implant a deep brain stimulator, the preoperative data held in the electronic medical record are not automatically transferred to the surgical planning station. Data surrounding the implantation of a deep brain stimulator are not transferred from the surgical environment. The current method of data inclusion at many centers is manual entry, photography or scanning of documents, and manual upload of these items into the electronic health record if available. This type of surgery is performed by a neurosurgeon; however, follow-up is performed by a neurologist who in many cases was not present during the pre-op evaluation or in the operating room. When the neurologist meets the individual with disease for the first time in clinic for programming of the deep brain stimulator, in most cases, only limited data are available to the neurologist to assist in the programming of an enormous number of possible combinations of the, for example, quadrapolar implant device having a wide frequency amplitude and pulse width selection available. Further difficulties exist in measuring the outcome of these cohort members of individuals with disease after treatment. No readily available system allows the remote aggregation and integration of mobile medical device-acquired objective measurements along with diary data of these treated individuals with disease in the home environment.
There is limited ability to aggregate and track factors, which may lead to an improvement in surgical technique or individual with disease selection across either the entire or a selected cross-section of the population of individuals with disease treated. There is currently a delay in the meaningful use of the electronic health record for specialty care.
Referring briefly to FIG. 83, the typical current processes include manual data entry 8322, measurements of head position, photography of surgical sites, scanning of documents and manual upload to an electronic health record. Database software may be incapable of performing data analysis such as might be accomplished by known content-based image recognition, data parsing and analysis of the manually input data, database query and retrieval and the like to effectively provide, for example, researchers or clinicians with the data they need to assist a given individual with disease or the population of all individuals with disease sharing the same disease or disorder.
Moreover, surgical follow-up may not be recorded in any database except at a paper level, or it may be recorded by a neurologist unfamiliar or not present at the original surgery or pre-operation individual with disease evaluation. There is limited ability to aggregate and track factors, which may lead to an improvement in surgical technique or individual with disease selection across either the entire or a selected cross-section of the population of individuals diagnosed with a disease treated. There is currently a delay in the meaningful use of the electronic health record (EHR) for specialty care (FIG. 4 showing a delay centered in 2012 between primary and specialty care). Significant hurdles exist to increased adoption of currently known therapies, compliance, and clinical translational research within PD and other restorative neurosciences ailments. Challenges in measuring, aggregating, reporting, and querying the rich dataset surrounding treatments within the restorative neurosciences as well as other areas of research or individual with disease tracking or treatment within neuroscience are subject to improvement with emerging technology.
Current systems designed for use by individuals diagnosed with disease and physicians to handle even a limited subset of the described data are not designed to answer important questions, which may improve healthcare. An example of this is there is no way in aggregate form for individuals diagnosed with a disease to track their outcomes, the battery life of their personal devices (medical, communication or combination), adverse side effects of the therapy or other important factors.
A system is known from US Published Patent Application No.'s 2012/0029369 published Feb. 2, 2012; 2009/0012417 published Jan. 8, 2009 and 2002/0126731 whereby a passive microwave receiver may obtain internal body temperature at various depths by frequency selection (with no active microwave transmission which may be dangerous) from known “black-body” radiation. Moreover, measures of blood flow and pressure may be possible from this apparatus in combination with known automatic apparatus for collection of blood pressure.
Social networks are known such as Facebook and Linked-In whereby individuals may share information about themselves with others. Friend and business relationships may develop from participation. Disease support groups have sites but these sites are typically anonymous and impersonal and may not permit similarly situated individuals to electronically participate outside of a support group.
In view of the above, there is clearly a need in the art for improved systems and methods for collecting objective data (as well as subjective data collected in a medical environment) of an individual diagnosed with disease at home or at a doctor's office. Given the foregoing, what are needed are systems, methods and computer program products that provide a framework to define a model-based multidimensional medical record that overcomes legal and technical hurdles, may span multiple computer systems, networks, and organizations, and supports new objective data collection as well as provides an input for sharing such multi-media data collection as photographic data and video/movie data as well as provide an opportunity for networking of those diagnosed with disease, disease treatment development, insurance carrier access, medical practitioner access, developer access and the like.